Abstract
PURPOSE: Quasi-diffusion MRI (QDI) is a novel quantitative technique based on the continuous time random walk model of diffusion dynamics. QDI provides estimates of the diffusion coefficient, D1,2 in mm(2) s(-1) and a fractional exponent, α , defining the non-Gaussianity of the diffusion signal decay. Here, the b-value selection for rapid clinical acquisition of QDI tensor imaging (QDTI) data is optimized. METHODS: Clinically appropriate QDTI acquisitions were optimized in healthy volunteers with respect to a multi-b-value reference (MbR) dataset comprising 29 diffusion-sensitized images arrayed between b = 0 and 5000 s mm(-2) . The effects of varying maximum b-value ( bmax ), number of b-value shells, and the effects of Rician noise were investigated. RESULTS: QDTI measures showed bmax dependence, most significantly for α in white matter, which monotonically decreased with higher bmax leading to improved tissue contrast. Optimized 2 b-value shell acquisitions showed small systematic differences in QDTI measures relative to MbR values, with overestimation of D1,2 and underestimation of α in white matter, and overestimation of D1,2 and α anisotropies in gray and white matter. Additional shells improved the accuracy, precision, and reliability of QDTI estimates with 3 and 4 shells at bmax = 5000 s mm(-2) , and 4 b-value shells at bmax = 3960 s mm(-2) , providing minimal bias in D1,2 and α compared to the MbR. CONCLUSION: A highly detailed optimization of non-Gaussian dMRI for in vivo brain imaging was performed. QDI provided robust parameterization of non-Gaussian diffusion signal decay in clinically feasible imaging times with high reliability, accuracy, and precision of QDTI measures.